Social illness

Reflections on the MQ Datamind Workshop and Conference by an Early Career Researcher

Mao Fong Lim, NIHR Academic Clinical Fellow, shares their experiences attending September’s Data Science meeting and workshop.

We began with an unexpected scene for a data science workshop on mental health – the office of Deutsche Bank in Central London. This was followed by an equally surprising line from Marcos del Pozo Banos: “I don’t know how to write code in R or Python”. 

This was both amusing and candid, but as I gradually discovered through the course of the workshop – very realistic. 

A bit about myself – I am an NIHR Academic Clinical Fellow in Psychiatry. Still, I consider myself a very early career researcher (ECR), especially in data science. My research aims to explore the immunological relationship between physical and mental health. In doing so, I am turning to big data for answers and climbing the steep learning curve that accompanies this pursuit.

I have always found programming extremely daunting. I attended the MQ Datamind workshop to kickstart my foray into data science. Although academics and informaticians made up most of the audience, Marcos made it accessible. He introduced programming as essentially the task of communicating with a computer in numbers. 

I found Marcos’ worksheet/exercise on Kaggle to be a real insight into a programmer’s mind and a helpful resource to return to in the future. The whole process was broken down into small steps, interspersed with statements: “now I will search for relevant code on the internet”. Several researchers I have spoken to since have confirmed that this is, indeed, how they code in R! 

But of course – data science is more than just crunching numbers and writing (or looking for code). Even on the workshop day, we were introduced to some fantastic examples of how NHS clinical care and research could be transformed by embedding processes, people, and expertise into existing processes (Johnny Downs and Pauline Whelan).

The conference the next day offered even more insights into data science. Greg Farber from the NIMH posed several vital questions about the interoperability of the data we collect and efforts around coordinating this. This was further echoed by the Mental Health Funders panel (Wellcome Trust, MQ, NIHR), which discussed the concerted efforts of funding bodies in facilitating data interoperability and various data science initiatives such as the Wellcome Data Prize. As an ECR, it was particularly fascinating to gain insight into the decision-making process of funding bodies and the (often dreaded) process of grant and fellowship applications. 

Andrew Morris of Health Data Research UK (HDR UK) then gave an inspiring insight into the data science infrastructure available in the UK. Linking back to previous themes of data interoperability, we were introduced to the impact that HDR UK has had on healthcare, particularly in COVID-19, by providing massive datasets that enabled ground-breaking research.

We were then introduced to how data is collected through the fantastic examples of MindKind (Mina Fazel) and the GLAD Study (Thalia Eley). MindKind was a great example of coproduction, involving young people in how and what data is collected and how that data is used. Both MindKind and GLAD were stellar examples of how to harness the reach of social media while being aware of the pitfalls, such as the potential skewing of recruitment.  

I found it fascinating to see so many applications of data science in mental health. Particular highlights for me were: 

  • Linking social care and health records (DWP, children in care)
  • Risk prediction models for mental health outcomes (Emmanuele Osimo, Ben Perry)
  • Well-designed posters and presentation styles of fellow researchers. (Special mention to Max Taquet for a very slick and accessible presentation on his findings around COVID-19 brain fog).

Finally, I left with an important reminder that beneath the veneer of (mostly) agnostic data – are real lives. As Ann John said while introducing John Niven, we must remember that in data science, we are using public money and data entrusted to us by real people who have real lives that will be impacted by the findings and implementation of our research. John gifted us with an evocative, honest and heartbreaking account of his brother’s death by suicide over a decade ago, which brought about tears and at least one standing ovation.

Overall, it was a fantastic workshop and conference that far exceeded my expectations and one that I would highly recommend to fellow ECRs. On a personal note, I have now received a large dataset that I will work on as part of my first project. I will take up a course on Mendelian Randomisation, which will rely a lot on my rudimentary programming skills. I am scoping out national and local opportunities for data that might be helpful for my future projects. I hope to have something to share at the next MQ DataMind event! 


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